Virtual colonoscopy is a noninvasive technique for human colon cancer screening. Computed tomography (CT) or Magnetic resonance (MR) techniques are used to generate high-resolution cross-sectional images of the inner surface of the colon. The techniques are presently of importance in the field of medicine
Both CT and MR colonography generate a large number of images that must be interpreted by a radiologist for the presence of polyps; see Arie E. Kaufman, Sarang Lakare, Kevin Kreeger, Ingmar Bitter, Virtual Colonoscopy, Communications of the ACM, vol 48, No. 2, pp. 37-41, 2005; and the paper by Macari, Lavelle, Berman, and Megibow cited in the next paragraph.
Commonly used methods to examine these datasets include slice-by-slice viewing, referred to as primary 2-dimensional (2D) reading and virtual flythroughs referred to as primary 3-dimensional (3D) reading. There appears to be little agreement in the literature as to which method results in the greatest rate of polyp detection; see Hara A. K., Johnson C. D., Reed J. E., Ehman R. L., Ilsrtup D. M., Colorectal polyp detection with CT Colonography, two-versus three dimensional techniques, Radiology, 1996, 200:49-54; Macari M, Milano A, Lavelle M, Berman P, Megibow A J. Comparison of time-efficient CT colonography with two- and three-dimensional colonic evaluation for detecting colorectal polyps, AJR Am J Roentgenol. 2000, 174:1543-9; Macari M, Lee J, Garcia Figueiras R, Megibow A, Bennett G, Badd J, Primary 2D versus 3D Interpretation Techniques Using Thin Section Multi-Detector Row CT Colonography (CTC), RSNA, 2004.
A number of techniques have been proposed to facilitate 3D reading. Most of these techniques automate the navigation process by calculating the colonic centerline; see for example, U.S. patent application Ser. No. 10/842,972, filed May 11, 2004 in the name of Boissonnat, Jean-Daniel and Geiger, Bernhard and entitled METHOD AND APPARATUS FOR FAST AUTOMATIC CENTERLINE EXTRACTION FOR VIRTUAL ENDOSCOPY whereof the disclosure is incorporated herein by reference; and Robert J. T. Sadleir, Paul F. Whelan, Colon Centerline Calculation for CT Colonography using Optimised 3D Topological Thinning, 1st International Symposium on 3D Data Processing Visualization and Transmission (3DPVT'02), pp. 800-804, 2002; I. Bitter, M. Sato, M. Bender, A. Kaufman, M. Wan, CEASAR: A Smooth, Accurate and Robust Centerline Extraction Algorithm, In Proc. IEEE Visualisation, 2000; R. Chiou, A. Kaufman, Z. Liang, L. Hong, and M. Achniotou, Interactive Fly-Path Planning Using Potential Fields and Cell Decomposition for Virtual Endoscopy,” IEEE Trans Nuclear Sciences, vol. 46, no. 4, pp. 1045-1049, 1999; and Samara Y., Fiebich M., Dachman A. H., Kuniyoshi J. K., Doi K., Hoffmann K. R., Automated calculation of the centerline of the human colon on CT images, Acad Radiol. 1999 June; 6(6): 352-9.
Other techniques automate the navigation process by computing the longest ray cast along the view direction. See, for example, U.S. patent application Ser. No. 10/322,326, filed Dec. 18, 2002 in the name of B. Geiger, and entitled AUTOMATIC NAVIGATION FOR VIRTUAL ENDOSCOPY, whereof the disclosure is incorporated herein by reference.
Another valuable help for 3D reading is the availability of techniques to get a map of colon wall patches that have not been observed during flythrough. Frequently, such areas are between deep Haustral folds. Such techniques have been proposed by, for example, F. M. Vos et. al. “A new visualization method for virtual colonoscopy”, Lecture Notes in Computer Science, vol. 2208, 2001. However, these techniques are limited to 3D flythrough.